"""
Threshold Network for NOR Gate

A formally verified single-neuron threshold network computing negated disjunction.
Weights are integer-constrained and activation uses the Heaviside step function.
NOR is functionally complete - any Boolean function can be built from NOR gates.
"""

import torch
from safetensors.torch import load_file


class ThresholdNOR:
    """
    NOR gate implemented as a threshold neuron.

    Circuit: output = (w1*x1 + w2*x2 + bias >= 0)
    With weights=[-1,-1], bias=0: fires only when both inputs are 0.
    """

    def __init__(self, weights_dict):
        self.weight = weights_dict['weight']
        self.bias = weights_dict['bias']

    def __call__(self, x1, x2):
        inputs = torch.tensor([float(x1), float(x2)])
        weighted_sum = (inputs * self.weight).sum() + self.bias
        return (weighted_sum >= 0).float()

    @classmethod
    def from_safetensors(cls, path="model.safetensors"):
        return cls(load_file(path))


def forward(x, weights):
    """
    Forward pass with Heaviside activation.

    Args:
        x: Input tensor of shape [..., 2]
        weights: Dict with 'weight' and 'bias' tensors

    Returns:
        NOR(x[0], x[1])
    """
    x = torch.as_tensor(x, dtype=torch.float32)
    weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
    return (weighted_sum >= 0).float()


if __name__ == "__main__":
    weights = load_file("model.safetensors")
    model = ThresholdNOR(weights)

    print("NOR Gate Truth Table:")
    print("-" * 25)
    for x1 in [0, 1]:
        for x2 in [0, 1]:
            out = int(model(x1, x2).item())
            expected = 1 - (x1 | x2)
            status = "OK" if out == expected else "FAIL"
            print(f"NOR({x1}, {x2}) = {out}  [{status}]")